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Predictions of nuclear models guide the design of nuclear facilities toensure their safe and efficient operation. Because nuclear models often do notperfectly reproduce available experimental data, decisions based on theirpredictions may not be optimal. Awareness about systematic deviations betweenmodels and experimental data helps to alleviate this problem. This paper showshow a sparse approximation to Gaussian processes can be used to estimate themodel bias over the complete nuclide chart at the example of inclusivedouble-differential neutron spectra for incident protons above 100\,MeV. Apowerful feature of the presented approach is the ability to predict the modelbias for energies, angles, and isotopes where data are missing. The number ofexperimental data points that can be taken into account is at least in theorder of magnitude of~$10^4$ thanks to the sparse approximation. The approachis applied to the Li\`ege Intranuclear Cascade Model (INCL) coupled to theevaporation code ABLA. The results suggest that sparse Gaussian processregression is a viable candidate to perform global and quantitative assessmentsof models. Limitations of a philosophical nature of this (and any other)approach are also discussed.

epj nuclear sciences and technologies

Estimating model bias over the complete nuclide chart with sparse  Gaussian processes at the example of INCL/ABLA and double-differential  neutron spectra